Difference between revisions of "Milestone 4 TuringMachine: Sustainable Reputation Mechanism; Insights from Finance Industry"

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(Influence and Related work)
(Influence and Related work)
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[http://crowdresearch.stanford.edu/w/img_auth.php/7/7f/The_future_of_crowd_work_%28private%29.pdf The Future of Work, Kittur etal 2013] and [http://sloanreview.mit.edu/article/the-collective-intelligence-genome/ Genomes of Collective Intelligence Framework, Malone etal 2010] have shown that Reputation plays a big role in developing sustainable crowd sourcing communities. There is huge risk involve in the crowd sourcing work. Both requestors and workers are exposed to the risk related to payment or quality.  
 
[http://crowdresearch.stanford.edu/w/img_auth.php/7/7f/The_future_of_crowd_work_%28private%29.pdf The Future of Work, Kittur etal 2013] and [http://sloanreview.mit.edu/article/the-collective-intelligence-genome/ Genomes of Collective Intelligence Framework, Malone etal 2010] have shown that Reputation plays a big role in developing sustainable crowd sourcing communities. There is huge risk involve in the crowd sourcing work. Both requestors and workers are exposed to the risk related to payment or quality.  
  
*How should workers select the tasks that would diversify the risk of payment defaults and maximize the gain?
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We build our idea of sustainable reputation using theories in structured finance and recommendation system. We propose diversificaiton of the portfolio to address following concerns:  
*How should requestors select the workers so that they would diversify the risk of quality default and maximize the gain?
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We build our idea of sustainable reputation using theories in structured finance and recommendation system.   
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*<u>Diversified Task Portfolio for Workers</u>: How should workers select the tasks that would diversify the risk of payment defaults and maximize the gain?
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*<u>Diversified Worker's Portfolio for Requestors</u>How should requestors select the workers so that they would diversify the risk of quality default and maximize the gain?
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[[File:Incentives.png|432px|center|Class]]
 
[[File:Incentives.png|432px|center|Class]]
  

Revision as of 17:32, 26 March 2015

Influence and Related work

The Future of Work, Kittur etal 2013 and Genomes of Collective Intelligence Framework, Malone etal 2010 have shown that Reputation plays a big role in developing sustainable crowd sourcing communities. There is huge risk involve in the crowd sourcing work. Both requestors and workers are exposed to the risk related to payment or quality.

We build our idea of sustainable reputation using theories in structured finance and recommendation system. We propose diversificaiton of the portfolio to address following concerns:

  • Diversified Task Portfolio for Workers: How should workers select the tasks that would diversify the risk of payment defaults and maximize the gain?
  • Diversified Worker's Portfolio for RequestorsHow should requestors select the workers so that they would diversify the risk of quality default and maximize the gain?


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Requestor-Worker Ratings

  • Workers and requestors rate each other.
  • An automated tranche creation algorithm clusters workers into A to E category.
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Tranches Structure Penalties, Incentives, Risk of Default

  • For the requestors in A tranche the minimum wage requirement is avg/below avg. However, for the requestors in D the minimum wage requirement is higher because he has a greater risk of default. Similar structure is designed for the worker. Please see figure 2.
  • Based on the ratings we can create the tranches of workers and requestors
  • This structure exposes the risk involved in the crowdsourcing platforms.
  • The workers can build the task portfolio using rating profiles and maximize the profit by diversifying the risk. For instance, a worker can select one task from tranch A, 3 from Avg, and rest from D.
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Automated Worker-Requestor Recommendations for Worker/Requestor Portfolio

Recommendation Systems

  • We combine Recommendation engine with Tranches and automatically match up requestors and workers.
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Combineing Global and Local Effects

  • Combine baseline method with latent factor model
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Latent Factor Model for Workers to Requestors Recommendation

  • Recommend workers to requestors
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